Overview

Dataset statistics

Number of variables 16
Number of observations 495
Missing cells 355
Missing cells (%) 4.5%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 62.0 KiB
Average record size in memory 128.3 B

Variable types

DateTime 1
Text 3
Numeric 8
Categorical 4

Alerts

Latitude is highly overall correlated with Region and 1 other fields High correlation
Longitude is highly overall correlated with Region and 1 other fields High correlation
Month1 is highly overall correlated with Month High correlation
Order is highly overall correlated with Season and 2 other fields High correlation
Season is highly overall correlated with Order and 1 other fields High correlation
Year is highly overall correlated with Order and 2 other fields High correlation
Month is highly overall correlated with Month1 and 1 other fields High correlation
Region is highly overall correlated with Latitude and 2 other fields High correlation
Show is highly overall correlated with Order and 2 other fields High correlation
State is highly overall correlated with Latitude and 2 other fields High correlation
State has 355 (71.7%) missing values Missing
Order is uniformly distributed Uniform
Order has unique values Unique

Reproduction

Analysis started 2023-07-27 23:19:53.759570
Analysis finished 2023-07-27 23:20:06.717343
Duration 12.96 seconds
Software version ydata-profiling vv4.3.2
Download configuration config.json

Variables

Air_Date
Date

Distinct 265
Distinct (%) 53.5%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
Minimum 2002-01-08 00:00:00
Maximum 2018-06-24 00:00:00
2023-07-27T18:20:06.844376 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:07.046261 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

City
Text

Distinct 392
Distinct (%) 79.2%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:07.302393 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Length

Max length 34
Median length 28
Mean length 8.7757576
Min length 3

Characters and Unicode

Total characters 4344
Distinct characters 72
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 321 ?
Unique (%) 64.8%

Sample

1st row Tokyo
2nd row Atami
3rd row Ho Chi Minh City
4th row Mekong River
5th row Phnom Penh
Value Count Frequency (%)
city 15
 
2.2%
new 9
 
1.3%
san 9
 
1.3%
tokyo 5
 
0.7%
singapore 5
 
0.7%
st 5
 
0.7%
york 5
 
0.7%
los 5
 
0.7%
orleans 4
 
0.6%
london 4
 
0.6%
Other values (475) 605
90.2%
2023-07-27T18:20:07.742681 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 511
 
11.8%
e 345
 
7.9%
n 340
 
7.8%
o 311
 
7.2%
i 283
 
6.5%
r 238
 
5.5%
t 201
 
4.6%
176
 
4.1%
s 171
 
3.9%
l 168
 
3.9%
Other values (62) 1600
36.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3466
79.8%
Uppercase Letter 674
 
15.5%
Space Separator 176
 
4.1%
Other Punctuation 15
 
0.3%
Dash Punctuation 12
 
0.3%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 511
14.7%
e 345
10.0%
n 340
9.8%
o 311
9.0%
i 283
 
8.2%
r 238
 
6.9%
t 201
 
5.8%
s 171
 
4.9%
l 168
 
4.8%
u 146
 
4.2%
Other values (30) 752
21.7%
Uppercase Letter
Value Count Frequency (%)
C 68
 
10.1%
P 66
 
9.8%
M 65
 
9.6%
S 65
 
9.6%
B 55
 
8.2%
A 42
 
6.2%
L 35
 
5.2%
T 30
 
4.5%
H 29
 
4.3%
K 27
 
4.0%
Other values (16) 192
28.5%
Other Punctuation
Value Count Frequency (%)
, 8
53.3%
. 5
33.3%
' 2
 
13.3%
Space Separator
Value Count Frequency (%)
176
100.0%
Dash Punctuation
Value Count Frequency (%)
- 12
100.0%
Final Punctuation
Value Count Frequency (%)
’ 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 4140
95.3%
Common 204
 
4.7%

Most frequent character per script

Latin
Value Count Frequency (%)
a 511
 
12.3%
e 345
 
8.3%
n 340
 
8.2%
o 311
 
7.5%
i 283
 
6.8%
r 238
 
5.7%
t 201
 
4.9%
s 171
 
4.1%
l 168
 
4.1%
u 146
 
3.5%
Other values (56) 1426
34.4%
Common
Value Count Frequency (%)
176
86.3%
- 12
 
5.9%
, 8
 
3.9%
. 5
 
2.5%
' 2
 
1.0%
’ 1
 
0.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 4321
99.5%
None 17
 
0.4%
Latin Ext Additional 5
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 511
 
11.8%
e 345
 
8.0%
n 340
 
7.9%
o 311
 
7.2%
i 283
 
6.5%
r 238
 
5.5%
t 201
 
4.7%
176
 
4.1%
s 171
 
4.0%
l 168
 
3.9%
Other values (47) 1577
36.5%
None
Value Count Frequency (%)
í 3
17.6%
ó 2
11.8%
â 2
11.8%
è 2
11.8%
á 2
11.8%
é 2
11.8%
ã 2
11.8%
ç 1
 
5.9%
ș 1
 
5.9%
Latin Ext Additional
Value Count Frequency (%)
ả 1
20.0%
ị 1
20.0%
ế 1
20.0%
ầ 1
20.0%
ộ 1
20.0%
Punctuation
Value Count Frequency (%)
’ 1
100.0%

Country
Text

Distinct 86
Distinct (%) 17.4%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:07.981601 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Length

Max length 32
Median length 19
Mean length 9.1919192
Min length 4

Characters and Unicode

Total characters 4550
Distinct characters 48
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 24 ?
Unique (%) 4.8%

Sample

1st row Japan
2nd row Japan
3rd row Vietnam
4th row Vietnam
5th row Cambodia
Value Count Frequency (%)
united 152
21.6%
states 141
20.0%
france 21
 
3.0%
italy 20
 
2.8%
japan 16
 
2.3%
china 15
 
2.1%
vietnam 13
 
1.8%
mexico 12
 
1.7%
puerto 11
 
1.6%
rico 11
 
1.6%
Other values (92) 292
41.5%
2023-07-27T18:20:08.386243 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 559
12.3%
t 544
12.0%
e 466
 
10.2%
i 396
 
8.7%
n 384
 
8.4%
d 216
 
4.7%
209
 
4.6%
s 187
 
4.1%
S 171
 
3.8%
U 165
 
3.6%
Other values (38) 1253
27.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3650
80.2%
Uppercase Letter 690
 
15.2%
Space Separator 209
 
4.6%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 559
15.3%
t 544
14.9%
e 466
12.8%
i 396
10.8%
n 384
10.5%
d 216
 
5.9%
s 187
 
5.1%
r 142
 
3.9%
o 127
 
3.5%
c 94
 
2.6%
Other values (14) 535
14.7%
Uppercase Letter
Value Count Frequency (%)
S 171
24.8%
U 165
23.9%
C 55
 
8.0%
P 38
 
5.5%
I 37
 
5.4%
M 25
 
3.6%
F 25
 
3.6%
R 24
 
3.5%
J 19
 
2.8%
G 18
 
2.6%
Other values (12) 113
16.4%
Space Separator
Value Count Frequency (%)
209
100.0%
Dash Punctuation
Value Count Frequency (%)
- 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 4340
95.4%
Common 210
 
4.6%

Most frequent character per script

Latin
Value Count Frequency (%)
a 559
12.9%
t 544
12.5%
e 466
10.7%
i 396
 
9.1%
n 384
 
8.8%
d 216
 
5.0%
s 187
 
4.3%
S 171
 
3.9%
U 165
 
3.8%
r 142
 
3.3%
Other values (36) 1110
25.6%
Common
Value Count Frequency (%)
209
99.5%
- 1
 
0.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 4550
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 559
12.3%
t 544
12.0%
e 466
 
10.2%
i 396
 
8.7%
n 384
 
8.4%
d 216
 
4.7%
209
 
4.6%
s 187
 
4.1%
S 171
 
3.8%
U 165
 
3.6%
Other values (38) 1253
27.5%

Day
Real number (ℝ)

Distinct 31
Distinct (%) 6.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 15.086869
Minimum 1
Maximum 31
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:08.550200 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 7
median 15
Q3 23
95-th percentile 29
Maximum 31
Range 30
Interquartile range (IQR) 16

Descriptive statistics

Standard deviation 9.0127288
Coefficient of variation (CV) 0.59738896
Kurtosis -1.2462899
Mean 15.086869
Median Absolute Deviation (MAD) 8
Skewness 0.094502353
Sum 7468
Variance 81.229281
Monotonicity Not monotonic
2023-07-27T18:20:08.698995 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Value Count Frequency (%)
1 28
 
5.7%
29 26
 
5.3%
8 26
 
5.3%
15 25
 
5.1%
4 24
 
4.8%
5 22
 
4.4%
22 21
 
4.2%
28 20
 
4.0%
7 20
 
4.0%
26 18
 
3.6%
Other values (21) 265
53.5%
Value Count Frequency (%)
1 28
5.7%
2 12
2.4%
3 13
2.6%
4 24
4.8%
5 22
4.4%
6 6
 
1.2%
7 20
4.0%
8 26
5.3%
9 16
3.2%
10 18
3.6%
Value Count Frequency (%)
31 5
 
1.0%
30 9
 
1.8%
29 26
5.3%
28 20
4.0%
27 14
2.8%
26 18
3.6%
25 12
2.4%
24 12
2.4%
23 14
2.8%
22 21
4.2%

Episode
Real number (ℝ)

Distinct 24
Distinct (%) 4.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 7.4666667
Minimum 1
Maximum 25
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:08.850316 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 4
median 6
Q3 10
95-th percentile 17
Maximum 25
Range 24
Interquartile range (IQR) 6

Descriptive statistics

Standard deviation 4.9832107
Coefficient of variation (CV) 0.66739429
Kurtosis 0.13577258
Mean 7.4666667
Median Absolute Deviation (MAD) 3
Skewness 0.88239028
Sum 3696
Variance 24.832389
Monotonicity Not monotonic
2023-07-27T18:20:08.998575 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
Value Count Frequency (%)
6 52
10.5%
2 49
9.9%
3 43
 
8.7%
4 42
 
8.5%
5 41
 
8.3%
7 39
 
7.9%
8 38
 
7.7%
1 29
 
5.9%
9 29
 
5.9%
15 25
 
5.1%
Other values (14) 108
21.8%
Value Count Frequency (%)
1 29
5.9%
2 49
9.9%
3 43
8.7%
4 42
8.5%
5 41
8.3%
6 52
10.5%
7 39
7.9%
8 38
7.7%
9 29
5.9%
10 17
 
3.4%
Value Count Frequency (%)
25 1
 
0.2%
24 1
 
0.2%
22 1
 
0.2%
21 2
 
0.4%
20 5
 
1.0%
19 7
 
1.4%
18 5
 
1.0%
17 7
 
1.4%
16 10
 
2.0%
15 25
5.1%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct 400
Distinct (%) 80.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 25.165775
Minimum -77.846388
Maximum 64.126521
Zeros 0
Zeros (%) 0.0%
Negative 62
Negative (%) 12.5%
Memory size 4.0 KiB
2023-07-27T18:20:09.171280 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum -77.846388
5-th percentile -33.021672
Q1 16.830208
median 33.328618
Q3 40.842627
95-th percentile 50.334136
Maximum 64.126521
Range 141.97291
Interquartile range (IQR) 24.012419

Descriptive statistics

Standard deviation 23.941123
Coefficient of variation (CV) 0.95133661
Kurtosis 2.5735669
Mean 25.165775
Median Absolute Deviation (MAD) 11.129873
Skewness -1.5374302
Sum 12457.058
Variance 573.17736
Monotonicity Not monotonic
2023-07-27T18:20:09.340800 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
35.689487 5
 
1.0%
1.352083 5
 
1.0%
40.712775 5
 
1.0%
34.052234 4
 
0.8%
21.027764 4
 
0.8%
29.951066 4
 
0.8%
40.78306 4
 
0.8%
51.507351 4
 
0.8%
43.318334 3
 
0.6%
45.501689 3
 
0.6%
Other values (390) 454
91.7%
Value Count Frequency (%)
-77.846388 1
0.2%
-77.633703 1
0.2%
-77.554281 1
0.2%
-72.294011 1
0.2%
-43.532054 1
0.2%
-41.472233 1
0.2%
-41.31755 1
0.2%
-41.163458 1
0.2%
-37.813628 2
0.4%
-34.905408 1
0.2%
Value Count Frequency (%)
64.126521 1
 
0.2%
60.169856 1
 
0.2%
59.93428 3
0.6%
59.329323 1
 
0.2%
58.455121 1
 
0.2%
55.953252 1
 
0.2%
55.864237 2
0.4%
55.755826 2
0.4%
55.676097 1
 
0.2%
54.597285 1
 
0.2%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct 400
Distinct (%) 80.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -15.130647
Minimum -159.46917
Maximum 172.63622
Zeros 0
Zeros (%) 0.0%
Negative 282
Negative (%) 57.0%
Memory size 4.0 KiB
2023-07-27T18:20:09.527183 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum -159.46917
5-th percentile -118.24368
Q1 -79.41537
median -17.467686
Q3 35.20293
95-th percentile 126.97797
Maximum 172.63622
Range 332.10539
Interquartile range (IQR) 114.6183

Descriptive statistics

Standard deviation 80.729155
Coefficient of variation (CV) -5.3354728
Kurtosis -0.91765033
Mean -15.130647
Median Absolute Deviation (MAD) 58.979946
Skewness 0.45079061
Sum -7489.6702
Variance 6517.1965
Monotonicity Not monotonic
2023-07-27T18:20:09.700038 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
139.691706 5
 
1.0%
103.819836 5
 
1.0%
-74.005973 5
 
1.0%
-118.243685 4
 
0.8%
105.83416 4
 
0.8%
-90.071532 4
 
0.8%
-73.971249 4
 
0.8%
-0.127758 4
 
0.8%
-1.981231 3
 
0.6%
-73.567256 3
 
0.6%
Other values (390) 454
91.7%
Value Count Frequency (%)
-159.469167 1
0.2%
-158.071598 1
0.2%
-158.056896 1
0.2%
-157.858333 2
0.4%
-157.02263 1
0.2%
-149.558476 1
0.2%
-147.651301 1
0.2%
-139.013569 1
0.2%
-123.120738 1
0.2%
-123.100707 1
0.2%
Value Count Frequency (%)
172.636225 1
 
0.2%
166.66833 1
 
0.2%
166.16443 1
 
0.2%
162.8805 1
 
0.2%
151.209296 2
 
0.4%
144.963058 2
 
0.4%
141.354376 1
 
0.2%
139.883565 1
 
0.2%
139.691706 5
1.0%
139.071705 1
 
0.2%

Month1
Real number (ℝ)

HIGH CORRELATION 

Distinct 12
Distinct (%) 2.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 6.0282828
Minimum 1
Maximum 12
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:09.866511 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 4
median 6
Q3 8
95-th percentile 11
Maximum 12
Range 11
Interquartile range (IQR) 4

Descriptive statistics

Standard deviation 3.1157183
Coefficient of variation (CV) 0.51685005
Kurtosis -1.0625017
Mean 6.0282828
Median Absolute Deviation (MAD) 2
Skewness 0.026818014
Sum 2984
Variance 9.7077005
Monotonicity Not monotonic
2023-07-27T18:20:09.997126 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Value Count Frequency (%)
5 64
12.9%
8 59
11.9%
4 58
11.7%
1 51
10.3%
10 50
10.1%
7 44
8.9%
3 38
7.7%
6 33
6.7%
9 32
6.5%
11 31
6.3%
Other values (2) 35
7.1%
Value Count Frequency (%)
1 51
10.3%
2 26
5.3%
3 38
7.7%
4 58
11.7%
5 64
12.9%
6 33
6.7%
7 44
8.9%
8 59
11.9%
9 32
6.5%
10 50
10.1%
Value Count Frequency (%)
12 9
 
1.8%
11 31
6.3%
10 50
10.1%
9 32
6.5%
8 59
11.9%
7 44
8.9%
6 33
6.7%
5 64
12.9%
4 58
11.7%
3 38
7.7%

Month
Categorical

HIGH CORRELATION 

Distinct 12
Distinct (%) 2.4%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
May
64 
August
59 
April
58 
January
51 
October
50 
Other values (7)
213 

Length

Max length 9
Median length 7
Mean length 5.7717172
Min length 3

Characters and Unicode

Total characters 2857
Distinct characters 26
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row January
2nd row January
3rd row January
4th row January
5th row January

Common Values

Value Count Frequency (%)
May 64
12.9%
August 59
11.9%
April 58
11.7%
January 51
10.3%
October 50
10.1%
July 44
8.9%
March 38
7.7%
June 33
6.7%
September 32
6.5%
November 31
6.3%
Other values (2) 35
7.1%

Length

2023-07-27T18:20:10.151659 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
may 64
12.9%
august 59
11.9%
april 58
11.7%
january 51
10.3%
october 50
10.1%
july 44
8.9%
march 38
7.7%
june 33
6.7%
september 32
6.5%
november 31
6.3%
Other values (2) 35
7.1%

Most occurring characters

Value Count Frequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2362
82.7%
Uppercase Letter 495
 
17.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 321
13.6%
e 294
12.4%
u 272
11.5%
a 230
9.7%
y 185
 
7.8%
b 148
 
6.3%
t 141
 
6.0%
l 102
 
4.3%
c 97
 
4.1%
p 90
 
3.8%
Other values (8) 482
20.4%
Uppercase Letter
Value Count Frequency (%)
J 128
25.9%
A 117
23.6%
M 102
20.6%
O 50
 
10.1%
S 32
 
6.5%
N 31
 
6.3%
F 26
 
5.3%
D 9
 
1.8%

Most occurring scripts

Value Count Frequency (%)
Latin 2857
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 2857
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 321
 
11.2%
e 294
 
10.3%
u 272
 
9.5%
a 230
 
8.1%
y 185
 
6.5%
b 148
 
5.2%
t 141
 
4.9%
J 128
 
4.5%
A 117
 
4.1%
M 102
 
3.6%
Other values (16) 919
32.2%

Order
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct 495
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 248.62222
Minimum 1
Maximum 496
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:10.311738 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 25.7
Q1 124.5
median 249
Q3 372.5
95-th percentile 471.3
Maximum 496
Range 495
Interquartile range (IQR) 248

Descriptive statistics

Standard deviation 143.44625
Coefficient of variation (CV) 0.57696472
Kurtosis -1.2024171
Mean 248.62222
Median Absolute Deviation (MAD) 124
Skewness -0.0024140102
Sum 123068
Variance 20576.827
Monotonicity Not monotonic
2023-07-27T18:20:10.521426 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1 1
 
0.2%
342 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
335 1
 
0.2%
334 1
 
0.2%
333 1
 
0.2%
Other values (485) 485
98.0%
Value Count Frequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
Value Count Frequency (%)
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%
489 1
0.2%
488 1
0.2%
487 1
0.2%

Region
Categorical

HIGH CORRELATION 

Distinct 9
Distinct (%) 1.8%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
North America
159 
Asia
94 
Europe
94 
South America
42 
Africa
32 
Other values (4)
74 

Length

Max length 15
Median length 13
Mean length 9.3474747
Min length 4

Characters and Unicode

Total characters 4627
Distinct characters 24
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Asia
2nd row Asia
3rd row Asia
4th row Asia
5th row Asia

Common Values

Value Count Frequency (%)
North America 159
32.1%
Asia 94
19.0%
Europe 94
19.0%
South America 42
 
8.5%
Africa 32
 
6.5%
Central America 32
 
6.5%
Middle East 24
 
4.8%
Oceania 14
 
2.8%
Antarctica 4
 
0.8%

Length

2023-07-27T18:20:10.707448 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-27T18:20:11.211637 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Value Count Frequency (%)
america 233
31.0%
north 159
21.1%
asia 94
12.5%
europe 94
12.5%
south 42
 
5.6%
africa 32
 
4.3%
central 32
 
4.3%
middle 24
 
3.2%
east 24
 
3.2%
oceania 14
 
1.9%

Most occurring characters

Value Count Frequency (%)
r 554
12.0%
a 451
9.7%
i 401
 
8.7%
e 397
 
8.6%
A 363
 
7.8%
o 295
 
6.4%
c 287
 
6.2%
t 265
 
5.7%
257
 
5.6%
m 233
 
5.0%
Other values (14) 1124
24.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3618
78.2%
Uppercase Letter 752
 
16.3%
Space Separator 257
 
5.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 554
15.3%
a 451
12.5%
i 401
11.1%
e 397
11.0%
o 295
8.2%
c 287
7.9%
t 265
7.3%
m 233
6.4%
h 201
 
5.6%
u 136
 
3.8%
Other values (6) 398
11.0%
Uppercase Letter
Value Count Frequency (%)
A 363
48.3%
N 159
21.1%
E 118
 
15.7%
S 42
 
5.6%
C 32
 
4.3%
M 24
 
3.2%
O 14
 
1.9%
Space Separator
Value Count Frequency (%)
257
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 4370
94.4%
Common 257
 
5.6%

Most frequent character per script

Latin
Value Count Frequency (%)
r 554
12.7%
a 451
10.3%
i 401
9.2%
e 397
9.1%
A 363
 
8.3%
o 295
 
6.8%
c 287
 
6.6%
t 265
 
6.1%
m 233
 
5.3%
h 201
 
4.6%
Other values (13) 923
21.1%
Common
Value Count Frequency (%)
257
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 4627
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 554
12.0%
a 451
9.7%
i 401
 
8.7%
e 397
 
8.6%
A 363
 
7.8%
o 295
 
6.4%
c 287
 
6.2%
t 265
 
5.7%
257
 
5.6%
m 233
 
5.0%
Other values (14) 1124
24.3%

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct 11
Distinct (%) 2.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 4.6505051
Minimum 1
Maximum 11
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:11.374710 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 1
5-th percentile 1
Q1 2
median 4
Q3 7
95-th percentile 10
Maximum 11
Range 10
Interquartile range (IQR) 5

Descriptive statistics

Standard deviation 2.9587369
Coefficient of variation (CV) 0.63621841
Kurtosis -0.80999977
Mean 4.6505051
Median Absolute Deviation (MAD) 2
Skewness 0.51997948
Sum 2302
Variance 8.7541242
Monotonicity Not monotonic
2023-07-27T18:20:11.501845 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Value Count Frequency (%)
2 83
16.8%
1 79
16.0%
4 62
12.5%
5 48
9.7%
3 44
8.9%
6 42
8.5%
7 39
7.9%
9 31
 
6.3%
8 29
 
5.9%
11 21
 
4.2%
Value Count Frequency (%)
1 79
16.0%
2 83
16.8%
3 44
8.9%
4 62
12.5%
5 48
9.7%
6 42
8.5%
7 39
7.9%
8 29
 
5.9%
9 31
 
6.3%
10 17
 
3.4%
Value Count Frequency (%)
11 21
 
4.2%
10 17
 
3.4%
9 31
 
6.3%
8 29
 
5.9%
7 39
7.9%
6 42
8.5%
5 48
9.7%
4 62
12.5%
3 44
8.9%
2 83
16.8%

Show
Categorical

HIGH CORRELATION 

Distinct 4
Distinct (%) 0.8%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
No Reservations
275 
Parts Unknown
158 
A Cook's Tour
42 
The Layover
 
20

Length

Max length 15
Median length 15
Mean length 14.030303
Min length 11

Characters and Unicode

Total characters 6945
Distinct characters 24
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row A Cook's Tour
2nd row A Cook's Tour
3rd row A Cook's Tour
4th row A Cook's Tour
5th row A Cook's Tour

Common Values

Value Count Frequency (%)
No Reservations 275
55.6%
Parts Unknown 158
31.9%
A Cook's Tour 42
 
8.5%
The Layover 20
 
4.0%

Length

2023-07-27T18:20:11.663658 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-27T18:20:11.842906 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Value Count Frequency (%)
no 275
26.6%
reservations 275
26.6%
parts 158
15.3%
unknown 158
15.3%
a 42
 
4.1%
cook's 42
 
4.1%
tour 42
 
4.1%
the 20
 
1.9%
layover 20
 
1.9%

Most occurring characters

Value Count Frequency (%)
o 854
12.3%
s 750
10.8%
n 749
10.8%
e 590
 
8.5%
537
 
7.7%
r 495
 
7.1%
a 453
 
6.5%
t 433
 
6.2%
v 295
 
4.2%
N 275
 
4.0%
Other values (14) 1514
21.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 5334
76.8%
Uppercase Letter 1032
 
14.9%
Space Separator 537
 
7.7%
Other Punctuation 42
 
0.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 854
16.0%
s 750
14.1%
n 749
14.0%
e 590
11.1%
r 495
9.3%
a 453
8.5%
t 433
8.1%
v 295
 
5.5%
i 275
 
5.2%
k 200
 
3.7%
Other values (4) 240
 
4.5%
Uppercase Letter
Value Count Frequency (%)
N 275
26.6%
R 275
26.6%
P 158
15.3%
U 158
15.3%
T 62
 
6.0%
A 42
 
4.1%
C 42
 
4.1%
L 20
 
1.9%
Space Separator
Value Count Frequency (%)
537
100.0%
Other Punctuation
Value Count Frequency (%)
' 42
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 6366
91.7%
Common 579
 
8.3%

Most frequent character per script

Latin
Value Count Frequency (%)
o 854
13.4%
s 750
11.8%
n 749
11.8%
e 590
9.3%
r 495
7.8%
a 453
 
7.1%
t 433
 
6.8%
v 295
 
4.6%
N 275
 
4.3%
R 275
 
4.3%
Other values (12) 1197
18.8%
Common
Value Count Frequency (%)
537
92.7%
' 42
 
7.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 6945
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 854
12.3%
s 750
10.8%
n 749
10.8%
e 590
 
8.5%
537
 
7.7%
r 495
 
7.1%
a 453
 
6.5%
t 433
 
6.2%
v 295
 
4.2%
N 275
 
4.0%
Other values (14) 1514
21.8%

State
Categorical

HIGH CORRELATION  MISSING 

Distinct 34
Distinct (%) 24.3%
Missing 355
Missing (%) 71.7%
Memory size 4.0 KiB
California
22 
New York
18 
Texas
11 
New Jersey
11 
Louisiana
10 
Other values (29)
68 

Length

Max length 14
Median length 12.5
Mean length 8.7142857
Min length 4

Characters and Unicode

Total characters 1220
Distinct characters 43
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 10 ?
Unique (%) 7.1%

Sample

1st row California
2nd row California
3rd row California
4th row New York
5th row New York

Common Values

Value Count Frequency (%)
California 22
 
4.4%
New York 18
 
3.6%
Texas 11
 
2.2%
New Jersey 11
 
2.2%
Louisiana 10
 
2.0%
Hawaii 6
 
1.2%
West Virginia 5
 
1.0%
New Mexico 5
 
1.0%
Montana 4
 
0.8%
Massachusetts 4
 
0.8%
Other values (24) 44
 
8.9%
(Missing) 355
71.7%

Length

2023-07-27T18:20:11.999125 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
new 34
18.6%
california 22
 
12.0%
york 18
 
9.8%
texas 11
 
6.0%
jersey 11
 
6.0%
louisiana 10
 
5.5%
hawaii 6
 
3.3%
virginia 6
 
3.3%
west 5
 
2.7%
mexico 5
 
2.7%
Other values (26) 55
30.1%

Most occurring characters

Value Count Frequency (%)
a 150
12.3%
i 147
 
12.0%
e 96
 
7.9%
o 92
 
7.5%
n 87
 
7.1%
s 83
 
6.8%
r 74
 
6.1%
43
 
3.5%
w 40
 
3.3%
l 38
 
3.1%
Other values (33) 370
30.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 991
81.2%
Uppercase Letter 184
 
15.1%
Space Separator 43
 
3.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 150
15.1%
i 147
14.8%
e 96
9.7%
o 92
9.3%
n 87
8.8%
s 83
8.4%
r 74
7.5%
w 40
 
4.0%
l 38
 
3.8%
t 29
 
2.9%
Other values (12) 155
15.6%
Uppercase Letter
Value Count Frequency (%)
N 37
20.1%
C 29
15.8%
M 25
13.6%
Y 18
9.8%
T 12
 
6.5%
J 11
 
6.0%
W 10
 
5.4%
L 10
 
5.4%
H 6
 
3.3%
V 6
 
3.3%
Other values (9) 20
10.9%
Space Separator
Value Count Frequency (%)
43
100.0%
Other Punctuation
Value Count Frequency (%)
. 2
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1175
96.3%
Common 45
 
3.7%

Most frequent character per script

Latin
Value Count Frequency (%)
a 150
12.8%
i 147
12.5%
e 96
 
8.2%
o 92
 
7.8%
n 87
 
7.4%
s 83
 
7.1%
r 74
 
6.3%
w 40
 
3.4%
l 38
 
3.2%
N 37
 
3.1%
Other values (31) 331
28.2%
Common
Value Count Frequency (%)
43
95.6%
. 2
 
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 1220
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 150
12.3%
i 147
 
12.0%
e 96
 
7.9%
o 92
 
7.5%
n 87
 
7.1%
s 83
 
6.8%
r 74
 
6.1%
43
 
3.5%
w 40
 
3.3%
l 38
 
3.1%
Other values (33) 370
30.3%

Title
Text

Distinct 235
Distinct (%) 47.5%
Missing 0
Missing (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:12.256434 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Length

Max length 46
Median length 36
Mean length 12.058586
Min length 3

Characters and Unicode

Total characters 5969
Distinct characters 64
Distinct categories 8 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 130 ?
Unique (%) 26.3%

Sample

1st row A Taste of Tokyo
2nd row Dining with Geishas
3rd row Cobra Heart - Foods That Make You Manly
4th row Eating on the Mekong
5th row Wild Delicacies
Value Count Frequency (%)
the 27
 
2.8%
new 24
 
2.5%
japan 14
 
1.5%
u.s 12
 
1.2%
southwest 12
 
1.2%
puerto 11
 
1.1%
rico 11
 
1.1%
jersey 11
 
1.1%
of 11
 
1.1%
island 11
 
1.1%
Other values (363) 818
85.0%
2023-07-27T18:20:12.676168 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 599
 
10.0%
467
 
7.8%
e 455
 
7.6%
i 402
 
6.7%
n 390
 
6.5%
o 362
 
6.1%
r 302
 
5.1%
t 248
 
4.2%
s 224
 
3.8%
l 188
 
3.1%
Other values (54) 2332
39.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 4416
74.0%
Uppercase Letter 956
 
16.0%
Space Separator 467
 
7.8%
Other Punctuation 107
 
1.8%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Dash Punctuation 6
 
0.1%
Decimal Number 3
 
0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 599
13.6%
e 455
10.3%
i 402
 
9.1%
n 390
 
8.8%
o 362
 
8.2%
r 302
 
6.8%
t 248
 
5.6%
s 224
 
5.1%
l 188
 
4.3%
u 184
 
4.2%
Other values (17) 1062
24.0%
Uppercase Letter
Value Count Frequency (%)
S 119
 
12.4%
C 86
 
9.0%
B 67
 
7.0%
M 64
 
6.7%
N 52
 
5.4%
P 47
 
4.9%
T 46
 
4.8%
A 45
 
4.7%
U 45
 
4.7%
I 41
 
4.3%
Other values (15) 344
36.0%
Other Punctuation
Value Count Frequency (%)
. 29
27.1%
, 29
27.1%
: 28
26.2%
/ 11
 
10.3%
& 5
 
4.7%
' 5
 
4.7%
Decimal Number
Value Count Frequency (%)
0 2
66.7%
1 1
33.3%
Space Separator
Value Count Frequency (%)
467
100.0%
Close Punctuation
Value Count Frequency (%)
) 7
100.0%
Open Punctuation
Value Count Frequency (%)
( 7
100.0%
Dash Punctuation
Value Count Frequency (%)
- 6
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 5372
90.0%
Common 597
 
10.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 599
 
11.2%
e 455
 
8.5%
i 402
 
7.5%
n 390
 
7.3%
o 362
 
6.7%
r 302
 
5.6%
t 248
 
4.6%
s 224
 
4.2%
l 188
 
3.5%
u 184
 
3.4%
Other values (42) 2018
37.6%
Common
Value Count Frequency (%)
467
78.2%
. 29
 
4.9%
, 29
 
4.9%
: 28
 
4.7%
/ 11
 
1.8%
) 7
 
1.2%
( 7
 
1.2%
- 6
 
1.0%
& 5
 
0.8%
' 5
 
0.8%
Other values (2) 3
 
0.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 5968
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 599
 
10.0%
467
 
7.8%
e 455
 
7.6%
i 402
 
6.7%
n 390
 
6.5%
o 362
 
6.1%
r 302
 
5.1%
t 248
 
4.2%
s 224
 
3.8%
l 188
 
3.2%
Other values (53) 2331
39.1%
None
Value Count Frequency (%)
ã 1
100.0%

Year
Real number (ℝ)

HIGH CORRELATION 

Distinct 16
Distinct (%) 3.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 2010.3131
Minimum 2002
Maximum 2018
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 4.0 KiB
2023-07-27T18:20:12.838766 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum 2002
5-th percentile 2002
Q1 2007
median 2010
Q3 2013
95-th percentile 2017
Maximum 2018
Range 16
Interquartile range (IQR) 6

Descriptive statistics

Standard deviation 4.3179469
Coefficient of variation (CV) 0.0021478977
Kurtosis -0.79035628
Mean 2010.3131
Median Absolute Deviation (MAD) 3
Skewness -0.06736882
Sum 995105
Variance 18.644665
Monotonicity Increasing
2023-07-27T18:20:12.988340 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Value Count Frequency (%)
2008 48
 
9.7%
2012 45
 
9.1%
2013 43
 
8.7%
2006 41
 
8.3%
2011 36
 
7.3%
2009 35
 
7.1%
2007 32
 
6.5%
2010 32
 
6.5%
2017 29
 
5.9%
2002 26
 
5.3%
Other values (6) 128
25.9%
Value Count Frequency (%)
2002 26
5.3%
2003 16
 
3.2%
2005 21
4.2%
2006 41
8.3%
2007 32
6.5%
2008 48
9.7%
2009 35
7.1%
2010 32
6.5%
2011 36
7.3%
2012 45
9.1%
Value Count Frequency (%)
2018 21
4.2%
2017 29
5.9%
2016 21
4.2%
2015 23
4.6%
2014 26
5.3%
2013 43
8.7%
2012 45
9.1%
2011 36
7.3%
2010 32
6.5%
2009 35
7.1%

Interactions

2023-07-27T18:20:04.945182 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:56.576771 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.803935 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.949929 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.125910 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.294261 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:02.674919 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.789858 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.102781 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:56.732928 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.943601 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.095561 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.277675 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.430531 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:02.807794 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.941227 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.252521 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:56.877347 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.087350 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.243870 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.418026 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.565669 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:02.944465 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.072523 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.410007 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.038794 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.240604 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.396294 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.567341 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.708927 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.091157 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.216666 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.570917 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.194938 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.383229 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.544372 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.710337 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.851539 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.236011 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.354809 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.720318 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.347384 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.523430 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.683866 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.854633 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.991165 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.378320 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.487653 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:05.868713 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.493749 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.658159 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.822568 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:00.998994 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:02.134209 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.506086 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.646791 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:06.011573 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:57.635810 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:58.792938 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:19:59.960564 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:01.137733 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:02.271273 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:03.637034 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
2023-07-27T18:20:04.780717 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-27T18:20:13.140825 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Day Episode Latitude Longitude Month1 Order Season Year Month Region Show State
Day 1.000 0.006 0.007 0.060 -0.088 0.027 -0.033 0.024 0.144 0.189 0.000 0.422
Episode 0.006 1.000 -0.020 -0.107 0.240 -0.210 -0.007 -0.249 0.350 0.180 0.255 0.377
Latitude 0.007 -0.020 1.000 -0.129 0.030 0.037 0.037 0.035 0.201 0.572 0.072 0.722
Longitude 0.060 -0.107 -0.129 1.000 0.030 0.004 0.027 0.003 0.169 0.650 0.099 0.875
Month1 -0.088 0.240 0.030 0.030 1.000 0.395 0.222 0.343 0.998 0.246 0.480 0.432
Order 0.027 -0.210 0.037 0.004 0.395 1.000 0.634 0.997 0.371 0.170 0.787 0.400
Season -0.033 -0.007 0.037 0.027 0.222 0.634 1.000 0.636 0.302 0.179 0.378 0.419
Year 0.024 -0.249 0.035 0.003 0.343 0.997 0.636 1.000 0.373 0.126 0.793 0.416
Month 0.144 0.350 0.201 0.169 0.998 0.371 0.302 0.373 1.000 0.249 0.539 0.441
Region 0.189 0.180 0.572 0.650 0.246 0.170 0.179 0.126 0.249 1.000 0.095 0.876
Show 0.000 0.255 0.072 0.099 0.480 0.787 0.378 0.793 0.539 0.095 1.000 0.130
State 0.422 0.377 0.722 0.875 0.432 0.400 0.419 0.416 0.441 0.876 0.130 1.000

Missing values

2023-07-27T18:20:06.266189 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-27T18:20:06.583967 image/svg+xml Matplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Air_Date City Country Day Episode Latitude Longitude Month1 Month Order Region Season Show State Title Year
0 2002-01-08 Tokyo Japan 8 1 35.689487 139.691706 1 January 1 Asia 1 A Cook's Tour NaN A Taste of Tokyo 2002
1 2002-01-12 Atami Japan 12 2 35.096276 139.071705 1 January 2 Asia 1 A Cook's Tour NaN Dining with Geishas 2002
2 2002-01-15 Ho Chi Minh City Vietnam 15 3 10.823099 106.629664 1 January 3 Asia 1 A Cook's Tour NaN Cobra Heart - Foods That Make You Manly 2002
3 2002-01-15 Mekong River Vietnam 15 4 15.933589 103.449284 1 January 4 Asia 1 A Cook's Tour NaN Eating on the Mekong 2002
4 2002-01-22 Phnom Penh Cambodia 22 5 11.556374 104.928210 1 January 5 Asia 1 A Cook's Tour NaN Wild Delicacies 2002
5 2002-01-29 Battambang Cambodia 29 6 13.095730 103.202205 1 January 6 Asia 1 A Cook's Tour NaN Eating on the Edge of Nowhere 2002
6 2002-01-29 Pailin Cambodia 29 6 12.909296 102.667557 1 January 7 Asia 1 A Cook's Tour NaN Eating on the Edge of Nowhere 2002
7 2002-01-29 Tokyo Japan 29 6 35.689487 139.691706 1 January 8 Asia 1 A Cook's Tour NaN Eating on the Edge of Nowhere 2002
8 2002-02-05 Porto Portugal 5 7 41.157944 -8.629105 2 February 9 Europe 1 A Cook's Tour NaN Cod Crazy 2002
9 2002-02-12 San Sebastian Spain 12 8 43.318334 -1.981231 2 February 10 Europe 1 A Cook's Tour NaN San Sebastian: A Food Lover's Town 2002
Air_Date City Country Day Episode Latitude Longitude Month1 Month Order Region Season Show State Title Year
485 2018-05-20 Yerevan Armenia 20 4 40.179186 44.499103 5 May 487 Asia 11 Parts Unknown NaN Armenia 2018
486 2018-06-03 Hong Kong China 3 5 22.396428 114.109497 6 June 488 Asia 11 Parts Unknown NaN Hong Kong 2018
487 2018-06-10 Berlin Germany 10 6 52.520007 13.404954 6 June 489 Europe 11 Parts Unknown NaN Berlin 2018
488 2018-06-17 Mamou United States 17 7 30.633809 -92.419299 6 June 490 North America 11 Parts Unknown Louisiana Cajun Mardi Gras 2018
489 2018-06-17 Grand Coteau United States 17 7 30.419920 -92.046509 6 June 491 North America 11 Parts Unknown Louisiana Cajun Mardi Gras 2018
490 2018-06-17 Lafayette United States 17 7 30.224090 -92.019843 6 June 492 North America 11 Parts Unknown Louisiana Cajun Mardi Gras 2018
491 2018-06-17 Opelousas United States 17 7 30.533530 -92.081509 6 June 493 North America 11 Parts Unknown Louisiana Cajun Mardi Gras 2018
492 2018-06-24 Bumthang Bhutan 24 8 27.641839 90.677305 6 June 494 Asia 11 Parts Unknown NaN Bhutan 2018
493 2018-06-24 Punakha Bhutan 24 8 27.592087 89.879746 6 June 495 Asia 11 Parts Unknown NaN Bhutan 2018
494 2018-06-24 Thimphu Bhutan 24 8 27.472792 89.639286 6 June 496 Asia 11 Parts Unknown NaN Bhutan 2018